Predicting How Pathogens Compete to Fight Outbreaks

In November, the University of Chicago Medicine conducted a combined disaster preparedness drill and flu vaccination drive to test its capability to respond to a pandemic. At the end of two days, more than 1,500 hospital employees received flu shots (not to mention 4,600 who already got one somewhere else), protecting them for the coming season.

Hospitals and public health departments design these efforts to reach as many people as possible, and use exercises like this to plan their response for treating people who do get sick. With the help of a grant from the University of Chicago, two scientists are working together to build models that can predict the extent of disease outbreaks, giving health officials even better tools to prepare for the coming flu season.

Sarah Cobey, PhD, assistant professor in the Department of Ecology and Evolution, and Jack Gilbert, PhD, associate professor and environmental microbiologist at Argonne National Laboratory, recently received $75,000 from the University of Chicago as part of the Strategic Collaborative Initiative. The program encourages improved collaboration between the mathematical and computational groups at Argonne and biomedical researchers at UChicago.

The grant will further Cobey’s disease ecology research by applying Gilbert’s work developing computational models to better understand how disease-causing pathogens like the influenza virus interact with each other.

Sarah Cobey, PhD

“If we know the major factors contributing to the different dynamics we observe between pathogens, we will have a better ability to predict what it is going to happen in the next season in a given location,” Cobey said.

If health officials could anticipate a rough flu season, like last winter when Chicago suffered through its worst in a decade, they could increase vaccination drives and beef up resources in emergency rooms and clinics. But right now they can make only a best estimate based on the history of cases in the area.

Cobey and Gilbert are taking a more data-driven approach. Using historical data on influenza cases, plus data on other less widespread diseases like pertussis (whooping cough) and the measles, they’re building statistical models to see how the different bugs compete with each other for hosts (that is, unlucky humans who end up getting sick).

“There are a lot of complex patterns that different diseases display, like how influenza tends to peak in the winter, and certain winters are dominated by certain strains,” Cobey said. “But we don’t really have a great way at this point to infer at the population level which of these forms of competition, or which of these interactions, are important.”

For example, there are three major strains of influenza that have been circulating in human populations since the late 1970’s: H3N2, H1N1, and B. From one season to the next, one or the other might be more prevalent. Researchers know that, say, if it’s a big H3N2 season, then the H1N1 and B strains tend to be less widespread, or vice versa.

These viruses clearly compete with each other, but no one has been able to describe exactly how strong that competition is. On top of that, there’s some evidence other respiratory viruses like rhinoviruses (which cause common colds) also compete with various strains of influenza and complicate the picture.

“It could be something as radical as changing the food distribution for a given population, or trying to alter air currents or improve green spaces in given areas that could significantly reduce the predicted likely outcome for given areas.” — Jack Gilbert

To get a handle on how these bugs influence each other, Cobey and Gilbert are starting with statistical models they know work well for single pathogens and adjusting them to accurately simulate interactions with other bugs. They’re also including climate variables like absolute humidity, and demographic statistics like birth and death rates in a given population to see how they contribute as well. These simulations will tell them which of these factors are most important, which can then be factored into predictions for future outbreaks.

“It’s not just how they will behave individually, but how they’ll behave in the context of each other,” Gilbert said. “Instead of predicting one disease we’re predicting all of the diseases in parallel.”

The tools Cobey and Gilbert are developing could also help researchers understand the potential impact of emerging viruses, like the new coronavirus discovered in the Middle East, or pockets of old diseases like measles and whooping cough that have spread in areas of the U.S. where people refuse to get vaccinated.

“It could be something as radical as changing the food distribution for a given population, or trying to alter air currents or improve green spaces in given areas that could significantly reduce the predicted likely outcome for given areas,” he said. “It’s not just getting at medical interventions but social and even city urban planning interventions that could interrupt cycles of disease relationships.”